A/B Testing Your Website: What to Test First and How to Read the Results

The best things to test first on a website are the elements people see and act on earliest: the headline, the main call to action and the layout of your highest-traffic page. Change one thing at a time, run the test long enough to see a clear pattern, and judge the result against a single goal such as enquiries or purchases.

A/B testing website pages is simple in principle. You show two versions to different visitors at the same time and compare which one gets more of the action you want. The difficulty lies in choosing sensible tests and reading the results honestly.

What is A/B testing on a website?

An A/B test compares a current page (the control, or version A) with a changed version (the variant, or version B). Visitors are split between the two at random. You then measure which version leads to more of a chosen outcome, such as form submissions, sign-ups or sales.

The point is to replace opinion with evidence. Instead of debating whether a green button or a blue one is better, you let visitor behaviour answer the question.

What should you test first?

Start where a small change can affect the most people and where the action matters most to the business. For most sites that means the pages with the highest traffic or the pages closest to a sale or enquiry.

1. Headlines

The headline is usually the first thing a visitor reads, and it decides whether they keep reading. Test a headline that states the benefit plainly against one that describes the product, or a shorter version against a longer one. Keep the rest of the page identical so you know the headline caused any difference.

2. Calls to action

The wording, position and prominence of your main button all matter. Examples worth testing:

• Specific wording ("Get a quote") against general wording ("Submit")
• A button placed above the fold against one placed after the first section of content
• One clear call to action against several competing ones

3. Page layouts

Layout tests are larger, so they take more care. You might move social proof closer to the top, shorten a long form, or change the order of sections. Because several things change at once, you learn which layout works better, but not necessarily why.

4. Forms and checkout steps

Every extra field asks something of the visitor. Testing a shorter form against a longer one is often revealing, particularly if the form is the main way people contact you.

How do you set up a test properly?

A reliable test needs a few things decided before it starts.

• A hypothesis. For example: "Using a benefit-led headline will increase enquiries because visitors will understand the offer sooner."
• One primary metric. Pick the single measure that defines success, such as completed enquiry forms.
• One change at a time. If you alter the headline and the button together, you cannot tell which made the difference.
• A random, simultaneous split. Both versions should run at the same time, so that a holiday or a promotion does not skew one of them.
• A planned duration. Decide in advance how long the test will run, and do not stop it the moment one version looks ahead.

How do you read the results?

Look first at whether the difference between versions is large enough, and consistent enough, to be more than chance. Most testing tools report a confidence level for this. A small gap on a small number of visitors can easily reverse if you keep going.

Some practical habits help:

• Let the test run for full weeks, so that weekday and weekend behaviour are both included.
• Check that enough people saw each version before drawing a conclusion.
• Look at the primary metric first, then at secondary effects such as bounce rate or order value.
• Record the result and what you learned, including tests that showed no difference. A flat result still tells you that the element may not matter much.

If a variant wins, make it the new control and test the next idea against it. Testing works best as a steady habit rather than a one-off.

How can machine learning help with A/B testing?

Machine learning can help with the parts of testing that are slow to do by hand. It can analyse large volumes of behaviour data to spot where visitors hesitate or leave, which points to better test ideas. It can also help segment results, showing that a variant works well for mobile visitors but not for desktop, for instance, which a single overall average would hide.

Some tools use these methods to shift traffic towards a better-performing version while a test is running. That can be useful, but it still needs a clear goal and human judgement about what the results mean for your brand and your customers. Our overview of conversion rate optimisation explains how testing fits into the wider process of improving a site.

What mistakes should you avoid?

• Stopping too early. Early leads are often noise. Wait until the planned duration has passed.
• Testing too many things at once. Overlapping tests on the same page can interfere with each other.
• Testing trivial changes on low-traffic pages. If few people visit, a test may never reach a clear answer. Choose bigger changes, or a more visited page.
• Ignoring the wider picture. A variant that boosts clicks but brings in poor-quality enquiries has not really won.
• Not tracking accurately. If your analytics is not recording conversions correctly, the results will mislead you. Privacy settings can affect this, as discussed in our piece on consent mode and data accuracy.

For background on how testing tools measure and report results, the Google Analytics help centre is a useful general reference.

Frequently asked questions

How long should an A/B test run?

Long enough to include full weeks of traffic and to gather a meaningful number of visitors on each version. Decide the duration before you start, and avoid ending the test early because one version looks ahead.

Can small websites run A/B tests?

Yes, but with less traffic it takes longer to reach a clear answer. Test bigger changes, such as a new headline or a simpler form, rather than small tweaks like button colour.

Should I test more than one thing at once?

For a standard A/B test, change one element at a time so you know what caused the result. Testing several changes together needs a different method and considerably more traffic.

What if the test shows no clear winner?

That is still a result. It suggests the element you changed has little effect on behaviour, so you can move on to a different idea with more potential.

Next steps

If you would like help deciding what to test on your own site, Virtuance Digital Marketing offers Conversion Rate Optimization (CRO), which uses machine learning to analyse user behaviour and test design variations. You can get in touch on +44 7852 620882 or at contact@virtuance.co.uk to talk it through.